Registration number: 19BCE1717
Faculty: Prof. Pavithra R
Slot: L55 + L56
Course code: CSE3020
rm(list=ls())
#1) Assign a decimal value to a variable A and display it.
A <- 10.5
print(A)
## [1] 10.5
#2) Print the class name of the variable A.
print(class(A))
## [1] "numeric"
#3) Check whether the variable A is of type 'numeric'.
print(is.numeric(A))
## [1] TRUE
#4) Assign an integer value to a variable B and display it.
B <- 5L
print(B)
## [1] 5
#5) Check whether the variable B is of type 'integer'.
print(is.integer(B))
## [1] TRUE
#6) Create a variable C which stores the integer part of variable A.
C=as.integer(A)
print(C)
## [1] 10
#7) Compute the cost of a chocolate.
cost <- C/B
print(cost)
## [1] 2
#8) Represent the money as character string.
as.character(A)
## [1] "10.5"
#9) Store the first name and last name of the kid.
fname="Ram"
lname="kumar"
print(is.integer(fname))
## [1] FALSE
#10) Display the message "Ram bought <B> chocolates"
print(sprintf("%s bought %d chocolates", paste(fname,lname), B))
## [1] "Ram kumar bought 5 chocolates"
#11) Extract the substring "Little" from the rhymes.
rhymes="Twinkle Twinkle Little Star"
print(substr(rhymes,start=17,stop=22))
## [1] "Little"
#12) Replace "Little" as Big.
print(sub("Little", "Big", rhymes))
## [1] "Twinkle Twinkle Big Star"
#13) Assign a complex number to a variable X.
X <- 2+4i
X
## [1] 2+4i
#14) Display the real part of X.
print(Re(X))
## [1] 2
#15) Display the imaginary part of X.
print(Im(X))
## [1] 4
#16) Compute square root of a negative number
print(sqrt(as.complex(-2))) #sqrt(-2+0i) also works
## [1] 0+1.414214i
#17) Check whether real part of X is greater than its imaginary part.
chk <- Re(X)>Im(X)
print(chk)
## [1] FALSE
rm(list=ls())
#1. Create vector 'class' to store the class names 'class1','class2',.,'class5'
class=c("class1", "class2","class3","class4","class5")
print(class)
## [1] "class1" "class2" "class3" "class4" "class5"
#2. Use assign() function to create a vector 'avg' to store the average marks.
assign("avg",c(63.5,72.3,88.9,65.4,79.8))
print(avg)
## [1] 63.5 72.3 88.9 65.4 79.8
#3. Display the average mark of class2.
print(avg[2])
## [1] 72.3
#4. Combine the vectors 'class' and 'avg' as details.
details=c(class,avg)
print(details)
## [1] "class1" "class2" "class3" "class4" "class5" "63.5" "72.3" "88.9"
## [9] "65.4" "79.8"
#5. Find the length of combined vector 'details'.
print(length(details))
## [1] 10
#6. Find the minimum average mark and print the class which scored it.
print(min(avg))
## [1] 63.5
print(class[which.min(avg)]) #which.min(avg)returns the index of the minimum value in vector avg
## [1] "class1"
#7. Find the maximum average mark and print the class which scored it.
print(max(avg))
## [1] 88.9
print(class[which.max(avg)])
## [1] "class3"
#8. Find the total of average marks scored by all classes.
print(sum(avg))
## [1] 369.9
#9. Find the mean of the average marks scored by all classes.
print(mean(avg))
## [1] 73.98
#10. Find the standard deviation of the average marks scored by all classes.
print(sd(avg))
## [1] 10.52079
#11. Arrange the average marks in ascending order.
print(sort(avg))
## [1] 63.5 65.4 72.3 79.8 88.9
#12. Create a vector classes by repeat the vector class twice.
classes <- rep(class,times=2)
print(classes)
## [1] "class1" "class2" "class3" "class4" "class5" "class1" "class2" "class3"
## [9] "class4" "class5"
#13. Create a vector marks by repeating each average mark twice.
marks <- rep(avg,each=2)
print(marks)
## [1] 63.5 63.5 72.3 72.3 88.9 88.9 65.4 65.4 79.8 79.8
#14. Create a sequence of 10 to 1. Add it to the vector avg and display it.
s=seq(10,1)
print(avg+s)
## [1] 73.5 81.3 96.9 72.4 85.8 68.5 76.3 91.9 67.4 80.8
#15. Create a vector bool that contains logical values 'TRUE' or 'FALSE' depending on the condition average marks>70.
bool=avg>70
print(class[bool])
## [1] "class2" "class3" "class5"
rm(list=ls())
#1. Represent the height information of a team of 12 basketball players as a matrix of dimension 4x3 in row major form.
height=matrix(c(162,173,160,181,159,176,183,162,181,160,170,172),nrow=4,ncol=3,byrow=TRUE)
print(height)
## [,1] [,2] [,3]
## [1,] 162 173 160
## [2,] 181 159 176
## [3,] 183 162 181
## [4,] 160 170 172
#2. Display the height at row 3 and column 2.
print(height[3,2])
## [1] 162
#3. Display all the heights in row 2.
print(height[2,])
## [1] 181 159 176
#Display all the heights in column 3.
print(height[,3])
## [1] 160 176 181 172
#5. Extract the heights in all rows but only in column 1 and 3.
print(height[,c(1,3)])
## [,1] [,2]
## [1,] 162 160
## [2,] 181 176
## [3,] 183 181
## [4,] 160 172
#6. Find the transpose of the matrix.
print(t(height))
## [,1] [,2] [,3] [,4]
## [1,] 162 181 183 160
## [2,] 173 159 162 170
## [3,] 160 176 181 172
#7. Four more players got added to the team. Update the matrix to reflect the heights of the players.
height=cbind(height,c(176,168,161,172))
print(height)
## [,1] [,2] [,3] [,4]
## [1,] 162 173 160 176
## [2,] 181 159 176 168
## [3,] 183 162 181 161
## [4,] 160 170 172 172
#8. Append four more players' height in the matrix.
height=rbind(height,c(175,162,170,165))
print(height)
## [,1] [,2] [,3] [,4]
## [1,] 162 173 160 176
## [2,] 181 159 176 168
## [3,] 183 162 181 161
## [4,] 160 170 172 172
## [5,] 175 162 170 165
# Root
print(sqrt(2))
## [1] 1.414214
# Log
print(log(2))
## [1] 0.6931472
# Assignment and arithmetic (+) operation
x = 5
y = 10
z <- x + y
print(z)
## [1] 15
# floor:
floor(5.555)
## [1] 5
# round:
round(5.555)
## [1] 6
v = seq(1,5, by=.5)
v
## [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
v1 = c(6,5,4,3,2,1)
v2 = c(10,9,8,7,6,5)
# Arithmetic operation on the vectors
v3 = v1 + v2
print(v3)
## [1] 16 14 12 10 8 6
vector = c(5,1,46,5,4,3,2,1)
sorted_vector <- sort(vector)
# Sort
sorted_vector
## [1] 1 1 2 3 4 5 5 46
# Rank
rank(vector)
## [1] 6.5 1.5 8.0 6.5 5.0 4.0 3.0 1.5
# Order
order(vector)
## [1] 2 8 7 6 5 1 4 3
# Which
which(vector == 16)
## integer(0)
# I have also used the paste and print functions here
print("Operations of V3 vector")
## [1] "Operations of V3 vector"
print(paste("Maximum = ", max(v3)))
## [1] "Maximum = 16"
print(paste("Minimum = ", min(v3)))
## [1] "Minimum = 6"
print(paste("Mean = ", mean(v3)))
## [1] "Mean = 11"
print(paste("Standard deviation = ", sd(v3)))
## [1] "Standard deviation = 3.74165738677394"
print(paste("Length of the vector V3 = ", length(v3)))
## [1] "Length of the vector V3 = 6"
#Without replacement
sample(1:40, 5)
## [1] 31 36 30 10 40
#With replacement
sample(1:50, 10, replace=T)
## [1] 14 7 36 12 22 23 24 7 18 4
a <- c(1, 2, 3, 4, 5)
b <- c(6, 7, 8, 9, 10)
r_matrix <- rbind(a, b)
r_matrix
## [,1] [,2] [,3] [,4] [,5]
## a 1 2 3 4 5
## b 6 7 8 9 10
c_matrix <- cbind(a, b)
c_matrix
## a b
## [1,] 1 6
## [2,] 2 7
## [3,] 3 8
## [4,] 4 9
## [5,] 5 10
rep(c(1:4), 3)
## [1] 1 2 3 4 1 2 3 4 1 2 3 4
gl(2, 10, length=20)
## [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
## Levels: 1 2
library("MASS")
data(iris)
head(iris, 5)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
students <- c("Makesh", "Anthra", "Bettina", "Chandhru", "Shyam")
event_1 <- c(10,10,6,8,10)
event_2 <- c(5,7,9,0,0)
event_3 <- c(7,9,0,8,6)
event_4 <- c(9,7,8,10,10)
data <- data.frame(students, event_1, event_2, event_3, event_4)
data
## students event_1 event_2 event_3 event_4
## 1 Makesh 10 5 7 9
## 2 Anthra 10 7 9 7
## 3 Bettina 6 9 0 8
## 4 Chandhru 8 0 8 10
## 5 Shyam 10 0 6 10
directory_read <- read.csv("sample.csv")
## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
## incomplete final line found by readTableHeader on 'sample.csv'
head(directory_read)
## students event_1 event_2 event_3 event_4 total_score
## 1 Kavya 10 5 7 9 31
## 2 Pushkar 10 7 9 7 33
## 3 Deepesh 6 9 0 8 23
Dimensions:
dim(iris)
## [1] 150 5
Structure:
str(iris)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
Use table() function:
table(iris$Sepal.Length)
##
## 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2
## 1 3 1 4 2 5 6 10 9 4 1 6 7 6 8 7 3 6 6 4
## 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.6 7.7 7.9
## 9 7 5 2 8 3 4 1 1 3 1 1 1 4 1
Names and attributes:
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
attributes(iris)
## $names
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
##
## $class
## [1] "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150
Tail and head (3) tuples:
head(iris, 3)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
tail(iris, 3)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
Summary:
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
Quantile:
quantile(iris$Sepal.Length)
## 0% 25% 50% 75% 100%
## 4.3 5.1 5.8 6.4 7.9
quantile(iris$Sepal.Length, c(.1, .3, .65)) # choose the quantiles
## 10% 30% 65%
## 4.80 5.27 6.20
Variance:
var(iris$Sepal.Length)
## [1] 0.6856935
Covariance:
cov(iris$Sepal.Length, iris$Petal.Length)
## [1] 1.274315
Covariance matrix:
cov(iris[,1:4])
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Sepal.Length 0.6856935 -0.0424340 1.2743154 0.5162707
## Sepal.Width -0.0424340 0.1899794 -0.3296564 -0.1216394
## Petal.Length 1.2743154 -0.3296564 3.1162779 1.2956094
## Petal.Width 0.5162707 -0.1216394 1.2956094 0.5810063
Correlation and correlation matrix:
cor(iris$Sepal.Length, iris$Petal.Length)
## [1] 0.8717538
cor(iris[,1:4])
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Sepal.Length 1.0000000 -0.1175698 0.8717538 0.8179411
## Sepal.Width -0.1175698 1.0000000 -0.4284401 -0.3661259
## Petal.Length 0.8717538 -0.4284401 1.0000000 0.9628654
## Petal.Width 0.8179411 -0.3661259 0.9628654 1.0000000
Aggregate:
aggregate(Sepal.Length ~ Species, summary, data=iris)
## Species Sepal.Length.Min. Sepal.Length.1st Qu. Sepal.Length.Median
## 1 setosa 4.300 4.800 5.000
## 2 versicolor 4.900 5.600 5.900
## 3 virginica 4.900 6.225 6.500
## Sepal.Length.Mean Sepal.Length.3rd Qu. Sepal.Length.Max.
## 1 5.006 5.200 5.800
## 2 5.936 6.300 7.000
## 3 6.588 6.900 7.900
m <- matrix(C<-(1:10),nrow=5, ncol=5)
#Matrix:
m
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 6 1 6 1
## [2,] 2 7 2 7 2
## [3,] 3 8 3 8 3
## [4,] 4 9 4 9 4
## [5,] 5 10 5 10 5
#Apply()
apply(m, 2, sum)
## [1] 15 40 15 40 15
names <- c("NAME1","NAME2","NAME3","NAME4")
names_lower <-lapply(names, tolower)
str(names_lower)
## List of 4
## $ : chr "name1"
## $ : chr "name2"
## $ : chr "name3"
## $ : chr "name4"
#same as lapply but a vector is returned
smn <- sapply(iris$Sepal.Length, min)
smn
## [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
## [19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0
## [37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5
## [55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
## [73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5
## [91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
## [109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2
## [127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8
## [145] 6.7 6.7 6.3 6.5 6.2 5.9
dbinom(2, 3, 0.60)
## [1] 0.432
dpois(2, 1)
## [1] 0.1839397
pnorm(150, 156, 4.6)
## [1] 0.09605751
hist(iris$Sepal.Length)
##### Density plot:
plot(density(iris$Sepal.Length))
##### Pie chart:
pie(table(iris$Species))
##### Barplot:
barplot(table(iris$Species))
##### Boxplot: Individual:
boxplot(iris$Sepal.Length)
Grouped:
boxplot(iris$Sepal.Length,iris$Sepal.Width)
boxplot(Sepal.Length~Species, data=iris)
##### Species-wise scatter plot:
with(iris, plot(Sepal.Length, Sepal.Width, col=Species, pch=as.numeric(Species)))
##### Pair plot:
pairs(iris)
# Import a library to view 3d plots
library("scatterplot3d")
## Warning: package 'scatterplot3d' was built under R version 4.0.2
scatterplot3d(iris$Petal.Width, iris$Sepal.Length, iris$Sepal.Width)
##### Heatmap
distMatrix <- as.matrix(dist(iris[,1:4]))
heatmap(distMatrix)
##### Contour plot:
filled.contour(volcano, color=terrain.colors, asp=1,plot.axes=contour(volcano, add=T))
##### Parallel coordinates:
parcoord(iris[1:4], col=iris$Species)
##### Parallel plots:
library(lattice)
parallelplot(~iris[1:4] | Species, data=iris)
plot(iris$Sepal.Length, iris$Sepal.Width, type="l")
par(mfrow=c(2,2))
hist(iris$Sepal.Length, main='Histogram',xlab='Sepal length', ylab ='Frequency', col=heat.colors(14))
boxplot(iris$Sepal.Length, iris$Sepal.Width, iris$Petal.Length, iris$Petal.Width, main='Iris boxplot of features', ylab='f',xlab='Shift', names = c('mm','mm','mm','mm'))
plot(iris$Sepal.Length,iris$Sepal.Width,main='Sepal length vs width', xlab='mm',ylab='mm',pch=2)
plot(iris$Petal.Length,iris$Sepal.Length,type="l",main='petal vs sepal length',xlab='mm',ylab='mm')
lines(iris$Petal.Length,iris$Sepal.Length,lty=2)
# I made a palindrome checker to perform all three things mentioned above.
isplaindrome <- function(n){
reverse = 0
buff = n
while (n > 0) {
r = n %% 10
reverse = reverse * 10 + r
n = n %/% 10
}
if (reverse == buff)
{
print(paste("It is palindrome!", reverse))
}
else{
print(paste("Not a palindrome", reverse))
}
}
## Call the function:
isplaindrome(12345) # should return "Not a palindrome"
## [1] "Not a palindrome 54321"
isplaindrome(123321) # should return "It is palindrome!"
## [1] "It is palindrome! 123321"
NOTE: The clustering parts are not yet covered in class and will be presented in the later lab exercises